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A Novel Method for the Parameterization of a Li-Ion Cell Model for EV/HEV Control Applications

机译:用于EV / HEV控制应用的锂离子电池模型参数化的新方法

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摘要

This paper presents a Li-ion cell model parameterization technique for hybrid electric and electric vehicle control applications. The proposed method is based on an equivalent electrical circuit (EEC) model of the Li-ion cell and combines the advantages of the two main strategies employed for cell model parameterization, namely, the offline and online procedures. Offline methods are based on the identification of relevant EEC parameter values using a limited set of test data specific to the target cell chemistry. Conversely, online techniques employ adaptive algorithms that update the cell model as it is being used. The novel method presented in this paper employs recurrent offline updates of the EEC parameterization set, and thus, it integrates the advantages of the offline approach, such as flexibility, reduced complexity, and improved run-time performance, with the main benefit of the online counterpart, which is the capacity to adapt the model parameterization to uncharacterized operating conditions. Based on an extensive set of experimental and simulation results obtained from tests specified in the IEC 62660-1 standard, it is shown that the proposed approach offers a significant accuracy improvement over simple offline methods, as well as enhanced runtime speed in comparison with commonly employed online strategies.
机译:本文提出了一种用于混合动力电动和电动汽车控制应用的锂离子电池模型参数化技术。所提出的方法基于锂离子电池的等效电路(EEC)模型,并结合了用于电池模型参数化的两种主要策略的优势,即离线过程和在线过程。离线方法基于使用特定于靶细胞化学性质的有限测试数据集对相关EEC参数值的识别。相反,在线技术采用自适应算法,该算法在使用细胞模型时会对其进行更新。本文提出的新颖方法采用了EEC参数化集的循环离线更新,因此,它结合了离线方法的优点(如灵活性,降低的复杂性和改进的运行时性能),并且具有在线获取的主要好处对应项,即使模型参数化适应未表征的运行条件的能力。根据从IEC 62660-1标准中指定的测试中获得的大量实验和模拟结果,表明,与常用的脱机方法相比,所提出的方法具有显着的精度提高,并且与常用方法相比,运行时速度有所提高在线策略。

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